code: def benchmark(test_image, compiled_model):
      # benchmark
      loop_num = 100
      warm_up = 8
      timeall = 0
      time_min = float("inf")
      time_max = float("-inf")
  
      for i in range(loop_num + warm_up):
          time0 = time.time()
          # perform the inference step
  
          output = compiled_model.infer_new_request({0: test_image})
          time1 = time.time()
          timed = time1 - time0
  
          if i >= warm_up:
              timeall = timeall + timed
              time_min = min(time_min, timed)
              time_max = max(time_max, timed)
  
      time_avg = timeall / loop_num
  
      print(
          "inference_time(ms): min={}, max={}, avg={}".format(
              round(time_min * 1000, 2),
              round(time_max * 1000, 1),
              round(time_avg * 1000, 1),
          )
      )
  
file_path: PaddleDetection/deploy/third_engine/demo_openvino/python/openvino_benchmark.py
imports: import cv2
  import numpy
  import time
  import argparse
  from scipy.special import softmax
  from openvino.runtime import Core
label_desc: Use image node_id 7 for calling a global function with example usage: benchmark(test_image, compiled_model) without return types